Abundance Tracking by Long-Read Nanopore Sequencing of Complex Microbial Communities in Samples from 20 Different Biogas/Wastewater Plants

Abstract Background: Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and are currently not cultivable, which makes abundance tracking difficult. Developments in nanopore long-read sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacteria and archaea composition, was established. Results: Samples from 20 different biogas/wastewater reactors were investigated, and a median of 20.5 Gb sequencing data per nanopore flow cell was retrieved for each reactor using the developed DNA isolation protocol. The nanopore sequencing data was compared against Illumina sequencing data while using different taxonomic indices for read classifications. The Genome Taxonomy Database (GTDB) index allowed sufficient characterisation of the abundance of bacteria and archaea in biogas reactors with a dramatic improvement (1.8- to 13-fold increase) in taxonomic classification compared to the RefSeq index. Both technologies performed similarly in taxonomic read classification with a slight advantage for Illumina in regards to the total proportion of classified reads. However, nanopore sequencing data revealed a higher genus richness after classification. Conclusion: Metagenomic read classification via nanopore provides a promising approach to monitor the abundance of taxa present in a microbial AD community, as an alternative to 16S rRNA studies or Illumina Sequencing.

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Abundance tracking by long-read nanopore sequencing of complex microbial communities in samples from 20 different biogas/wastewater plants

10.21203/rs.3.rs-31524/v1 ◽

2020 ◽

Author(s):

Christian Brandt ◽

Erik Bongcam-Rudloff ◽

Bettina Müller

Keyword(s):

Microbial Communities ◽

Illumina Sequencing ◽

Fold Increase ◽

Green Energy ◽

Dramatic Improvement ◽

Metagenomic Sequencing ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Long Read ◽

Genus Richness

Abstract Background: Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and are currently not cultivable, which makes abundance tracking difficult. Developments in nanopore long-read sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacteria and archaea composition, was established. Results: Samples from 20 different biogas/wastewater reactors were investigated, and a median of 20.5 Gb sequencing data per nanopore flow cell was retrieved for each reactor using the developed DNA isolation protocol. The nanopore sequencing data was compared against Illumina sequencing data while using different taxonomic indices for read classifications. The Genome Taxonomy Database (GTDB) index allowed sufficient characterisation of the abundance of bacteria and archaea in biogas reactors with a dramatic improvement (1.8- to 13-fold increase) in taxonomic classification compared to the RefSeq index. Both technologies performed similarly in taxonomic read classification with a slight advantage for Illumina in regards to the total proportion of classified reads. However, nanopore sequencing data revealed a higher genus richness after classification. Conclusion: Metagenomic read classification via nanopore provides a promising approach to monitor the abundance of taxa present in a microbial AD community, as an alternative to 16S rRNA studies or Illumina Sequencing.

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Abundance tracking by long-read nanopore sequencing of complex microbial communities in samples from 20 different biogas/wastewater plants

10.21203/rs.2.17734/v3 ◽

2020 ◽

Author(s):

Christian Brandt ◽

Erik Bongcam-Rudloff ◽

Bettina Müller

Keyword(s):

Microbial Communities ◽

Illumina Sequencing ◽

Fold Increase ◽

Green Energy ◽

Dramatic Improvement ◽

Metagenomic Sequencing ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Long Read ◽

Genus Richness

Abstract Background: Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and are currently not cultivable, which makes abundance tracking difficult. Developments in nanopore long-read sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacteria and archaea composition, was established. Results: Samples from 20 different biogas/wastewater reactors were investigated, and a median of 20.5 Gb sequencing data per nanopore flow cell was retrieved for each reactor using the developed DNA isolation protocol. The nanopore sequencing data was compared against Illumina sequencing data while using different taxonomic indices for read classifications. The Genome Taxonomy Database (GTDB) index allowed sufficient characterisation of the abundance of bacteria and archaea in biogas reactors with a dramatic improvement (1.8- to 13-fold increase) in taxonomic classification compared to the RefSeq index. Both technologies performed similarly in taxonomic read classification with a slight advantage for Illumina in regards to the total proportion of classified reads. However, nanopore sequencing data revealed a higher genus richness after classification. Conclusion: Metagenomic read classification via nanopore provides a promising approach to monitor the abundance of taxa present in a microbial AD community, as an alternative to 16S rRNA studies or Illumina Sequencing.

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Abundance tracking by long-read nanopore sequencing of complex microbial communities in samples from 20 different biogas/wastewater plants

10.21203/rs.2.17734/v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christian Brandt ◽

Erik Bongcam-Rudloff ◽

Bettina Müller

Keyword(s):

Microbial Communities ◽

Fold Increase ◽

Green Energy ◽

Dramatic Improvement ◽

Metagenomic Sequencing ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Long Reads ◽

Long Read ◽

Water Reactors

Abstract Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and not cultivable, which makes abundance tracking difficult. Developments in nanopore sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacterial composition, was established. Samples from 20 different biogas/waste-water reactors were investigated and a median of 20 Gb sequencing data per flow cell were retrieved for each reactor. Using the GTDB index allowed sufficient characterisation of abundance of bacteria and archaea in biogas reactors. A dramatic improvement (1.8- to 13-fold increase) in taxonomic classification was achieved using the GTDB-based index compared with the RefSeq index. Ongoing efforts in GTDB to achieve more phylogenetically coherent taxonomic species definitions, including meta-assembled genomes, give a clear advantage over conventional classification databases such as RefSeq. Unlike conventional 16S rRNA studies, metagenomic read classification allows abundance of the unknown microbial fraction to be monitored.

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Human gut microbial communities dictate efficacy of anti-PD-1 therapy in a humanized microbiome mouse model of glioma

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab023 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Kory J Dees ◽

Hyunmin Koo ◽

J Fraser Humphreys ◽

Joseph A Hakim ◽

David K Crossman ◽

...

Keyword(s):

Mouse Model ◽

Microbial Communities ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Microbial Composition ◽

Healthy Human ◽

Fecal Transplantation ◽

Host Interaction ◽

Shotgun Metagenomic Sequencing ◽

Mouse Lines

Abstract Background Although immunotherapy works well in glioblastoma (GBM) preclinical mouse models, the therapy has not demonstrated efficacy in humans. To address this anomaly, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a preclinical mouse model of GBM. Methods We used 5 healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate 5 independent humanized mouse lines (HuM1-HuM5). Results Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome with significant differences in diversity and microbial composition among HuM1-HuM5 lines. All HuM mouse lines were susceptible to GBM transplantation, and exhibited similar median survival ranging from 19 to 26 days. Interestingly, we found that HuM lines responded differently to the immune checkpoint inhibitor anti-PD-1. Specifically, we demonstrate that HuM1, HuM4, and HuM5 mice are nonresponders to anti-PD-1, while HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls. Bray-Curtis cluster analysis of the 5 HuM gut microbial communities revealed that responders HuM2 and HuM3 were closely related, and detailed taxonomic comparison analysis revealed that Bacteroides cellulosilyticus was commonly found in HuM2 and HuM3 with high abundances. Conclusions The results of our study establish the utility of humanized microbiome mice as avatars to delineate features of the host interaction with gut microbial communities needed for effective immunotherapy against GBM.

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IMMU-09. HUMAN MICROBIOTA INFLUENCE THE EFFICACY OF IMMUNOTHERAPY IN A MOUSE MODEL OF GLIOBLASTOMA

Neuro-Oncology ◽

10.1093/neuonc/noab196.369 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi93-vi94

Author(s):

Kory Dees ◽

Hyunmin Koo ◽

James Humphreys ◽

Joseph Hakim ◽

David Crossman ◽

...

Keyword(s):

Mouse Model ◽

Microbial Communities ◽

Mouse Models ◽

Gut Microbiome ◽

Positive Response ◽

Checkpoint Inhibitors ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Microbial Composition ◽

Healthy Human

Abstract Although immunotherapy works well in glioblastoma (GBM) pre-clinical mouse models, the therapy has unfortunately not demonstrated efficacy in humans. In melanoma and other cancers, the composition of the gut microbiome has been shown to determine responsiveness or resistance to immune checkpoint inhibitors (anti-PD-1). Most pre-clinical cancer studies have been done in mouse models using mouse gut microbiomes, but there are significant differences between mouse and human microbial gut compositions. To address this inconsistency, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a pre-clinical mouse model of GBM. We used five healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate five independent humanized mouse lines (HuM1-HuM5). Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome with significant differences in diversity and microbial composition among HuM1-HuM5 lines. Interestingly, we found that the HuM lines responded differently to anti-PD-1. Specifically, we demonstrate that HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls, while HuM1, HuM4, and HuM5 mice are resistant to anti-PD-1. These mice are genetically identical, and only differ in the composition of the gut microbiome. In a correlative experiment, we found that disrupting the responder HuM2 microbiome with antibiotics abrogated the positive response to anti-PD-1, indicating that HuM2 microbiota must be present in the mice to elicit the positive response to anti-PD-1 in the GBM model. The question remains of whether the “responsive” microbial communities in HuM2 and HuM3 can be therapeutically exploited and applicable in other tumor models, or if the “resistant” microbial communities in HuM1, HuM4, and HuM5 can be depleted and/or replaced. Future studies will assess responder microbial transplants as a method of enhancing immunotherapy.

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Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2

mBio ◽

10.1128/mbio.00122-11 ◽

2011 ◽

Vol 2 (4) ◽

Cited By ~ 322

Author(s):

Jizhong Zhou ◽

Ye Deng ◽

Feng Luo ◽

Zhili He ◽

Yunfeng Yang

Keyword(s):

Microbial Communities ◽

Microbial Ecology ◽

High Throughput ◽

Network Topology ◽

Ecosystem Functioning ◽

Soil Microbial Communities ◽

Microbial Populations ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Soil Microbial

ABSTRACT Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management. IMPORTANCE The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.

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Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

Frontiers in Genetics ◽

10.3389/fgene.2021.761791 ◽

2021 ◽

Vol 12 ◽

Author(s):

Davide Bolognini ◽

Alberto Magi

Keyword(s):

Variant Calling ◽

Research Report ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Factors Affecting ◽

Sequencing Technologies ◽

Long Reads ◽

Oxford Nanopore ◽

Sequencing Studies ◽

Long Read

Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.

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Cas9-Assisted Targeting of CHromosome segments (CATCH) for targeted nanopore sequencing and optical genome mapping

10.1101/110163 ◽

2017 ◽

Cited By ~ 5

Author(s):

Tslil Gabrieli ◽

Hila Sharim ◽

Yael Michaeli ◽

Yuval Ebenstein

Keyword(s):

Single Molecule ◽

Genome Mapping ◽

Single Point ◽

Read Length ◽

Whole Genome ◽

Sequencing Analysis ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Whole Genome Analysis ◽

Long Read

ABSTRACTVariations in the genetic code, from single point mutations to large structural or copy number alterations, influence susceptibility, onset, and progression of genetic diseases and tumor transformation. Next-generation sequencing analysis is unable to reliably capture aberrations larger than the typical sequencing read length of several hundred bases. Long-read, single-molecule sequencing methods such as SMRT and nanopore sequencing can address larger variations, but require costly whole genome analysis. Here we describe a method for isolation and enrichment of a large genomic region of interest for targeted analysis based on Cas9 excision of two sites flanking the target region and isolation of the excised DNA segment by pulsed field gel electrophoresis. The isolated target remains intact and is ideally suited for optical genome mapping and long-read sequencing at high coverage. In addition, analysis is performed directly on native genomic DNA that retains genetic and epigenetic composition without amplification bias. This method enables detection of mutations and structural variants as well as detailed analysis by generation of hybrid scaffolds composed of optical maps and sequencing data at a fraction of the cost of whole genome sequencing.

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Partner-independent fusion gene detection by multiplexed CRISPR/Cas9 enrichment and long-read Nanopore sequencing

10.1101/807545 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christina Stangl ◽

Sam de Blank ◽

Ivo Renkens ◽

Tamara Verbeek ◽

Jose Espejo Valle-Inclan ◽

...

Keyword(s):

Fusion Gene ◽

Diagnostic Methods ◽

Partner Choice ◽

Fusion Partner ◽

Nanopore Sequencing ◽

Fusion Genes ◽

Sequencing Data ◽

Gene Detection ◽

Detection Assay ◽

Long Read

AbstractFusion genes are hallmarks of various cancer types and important determinants for diagnosis, prognosis and treatment possibilities. The promiscuity of fusion genes with respect to partner choice and exact breakpoint-positions restricts their detection in the diagnostic setting, even for known and recurrent fusion gene configurations. To accurately identify these gene fusions in an unbiased manner, we developed FUDGE: a FUsion gene Detection assay from Gene Enrichment. FUDGE couples target-selected and strand-specific CRISPR/Cas9 activity for enrichment and detection of fusion gene drivers (e.g. BRAF, EWSR1, KMT2A/MLL) - without prior knowledge of fusion partner or breakpoint-location - to long-read Nanopore sequencing. FUDGE encompasses a dedicated bioinformatics approach (NanoFG) to detect fusion genes from Nanopore sequencing data. Our strategy is flexible with respect to target choice and enables multiplexed enrichment for simultaneous analysis of several genes in multiple samples in a single sequencing run. We observe on average a 508 fold on-target enrichment and identify fusion breakpoints at nucleotide resolution - all within two days. We demonstrate that FUDGE effectively identifies fusion genes in cancer cell lines, tumor samples and on whole genome amplified DNA irrespective of partner gene or breakpoint-position in 100% of cases. Furthermore, we show that FUDGE is superior to routine diagnostic methods for fusion gene detection. In summary, we have developed a rapid and versatile fusion gene detection assay, providing an unparalleled opportunity for pan-cancer detection of fusion genes in routine diagnostics.

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